Randomized Controlled Trial

The gold standard methodology for establishing causation: randomly assign subjects to treatment and control groups, apply the intervention to only the treatment group, and compare outcomes. Randomization is the key—it distributes all confounding variables (known and unknown) evenly between groups, isolating the effect of the intervention. Without randomization, you can only establish correlation, which is routinely mistaken for causation in business and policy.

When to use it

When evaluating causal claims from any source—research, consultants, case studies; when designing experiments to test business hypotheses (A/B tests, pilot programs); when distinguishing between correlation and causation in data analysis; when assessing whether a policy or strategy change actually caused observed outcomes.

How it can help

Whenever you claim 'X caused Y,' ask: was there a randomized controlled trial? If not, the causal claim is weaker than it appears. In business, A/B tests are simplified RCTs. The discipline is resisting the urge to draw causal conclusions from observational data. 'Companies that do X grow faster' doesn't mean X causes growth—it might mean successful companies can afford to do X (reverse causation) or that both are caused by a third factor.